normalizeData             package:CALIB             R Documentation

_N_o_r_m_a_l_i_z_a_t_i_o_n: _e_s_t_i_m_a_t_i_o_n _o_f _a_b_s_o_l_u_t_e _e_x_p_r_e_s_s_i_o_n _l_e_v_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     estimates absolute expression levels for each combination of a
     gene  and a tested biological condition.

_U_s_a_g_e:

     normalizeData(RG, parameter, array = array, condition = condition, dye = dye,
                   cloneid = cloneid, idcol = idcol)

_A_r_g_u_m_e_n_t_s:

      RG: an 'RGList_CALIB' object

parameter: a 'ParameterList' object

   array: integer vector specifying the index of the arrays.  Has
          length equal to two times of the number of arrays. 

condition: integer vector specifying the index of the conditions.  Has
          length equal to two times of the number of arrays. 

     dye: integer vector specifying the index of the dyes. Has length 
          equal to two times of the number of arrays. 

 cloneid: string vector specifying the clone ids of the clones to  be
          normalized. If missing, normalize all the clones.

   idcol: string specifying the column name of clone ids in the  genes
          field of RG. 

_D_e_t_a_i_l_s:

     This function estimates absolute expression levels for each
     combination of  a gene and a tested biological condition from the
     measured intensity.  It accepts measured intensities from 'RG'.

     The argument 'parameter' is an object of 'ParameterList'. The
     function accepts  model parameters from this argument.

     By using this function, for each combination of a gene and a
     tested biological condition, a single absolute expression level fo
     target is estimated. Therefore, specifying the design of
     experiment is necessary. Namely, the design of array,  condition
     and dye is needed. The three arguments 'array','condition'  and
     'dye' are three numeric vector to indicate the design of array, 
     condition and dye respectively. How to specify these three
     arguments refer to  the example below.

     The function is able to not only estimate all the genes on the
     slides but also estimate any gene on the slides seperately. The
     argument 'cloneid' accepts the clone ids of which the genes are
     interested by the user. If 'cloneid' argument is missing, the
     function will estimate all the genes on the slides.In order to 
     match clone id in the RG, column name which indicates clone ids in
     RG$genes should be specified by argument 'idcol'.

_V_a_l_u_e:

     a numeric matix containing the absolute expression levels. Columns
     indicate different conditions and rows indicate different genes.

_W_a_r_n_i_n_g:

     The function doesn't allow missing clone id. So please check
     before run the function.

_N_o_t_e:

     The main calculation part in this function is done by c++ code.

_A_u_t_h_o_r(_s):

     Hui Zhao

_R_e_f_e_r_e_n_c_e_s:

     Engelen, K., Naudts, B., DeMoor, B., Marchal, K. (2006)  A
     calibration method for estimating absolute expression levels from 
     microarray data. Bioinformatics  22: 1251-1258.

_E_x_a_m_p_l_e_s:

     # load data: RG and parameter
     data(RG)
     data(parameter)

     # define design matrix: two arrays, two condition and color-flip design
     array <- c(1,1,2,2)
     condition <- c(1,2,2,1)
     dye <- c(1,2,1,2)

     # specify clone-id column
     idcol <- "CLONE_ID"

     #data <- normalizeData(RG,parameter,array=array,condition=condition,dye=dye,idcol=idcol)

     ## only normalize a group of genes
     cloneid_interested <- c("250001", "250002", "250003", "250004", "250005")
     data <- normalizeData(RG,parameter,array=array,condition=condition,dye=dye,cloneid=cloneid_interested,idcol=idcol)

